{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:02:04Z","timestamp":1776272524317,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T00:00:00Z","timestamp":1696636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver\u2019s complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers\u2019 comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers\u2019 comprehensive emotion recognition. By combining the three modalities of a driver\u2019s voice, facial image, and video sequence, the six classification tasks of drivers\u2019 emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers\u2019 negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.<\/jats:p>","DOI":"10.3390\/s23198293","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T06:16:48Z","timestamp":1696832208000},"page":"8293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Drivers\u2019 Comprehensive Emotion Recognition Based on HAM"],"prefix":"10.3390","volume":"23","author":[{"given":"Dongmei","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yongjian","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Luhan","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"China Unicom Digital Technology Co., Ltd. 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